TY - JOUR

T1 - A data-driven methodological routine to identify key indicators for social-ecological system archetype mapping

AU - Pacheco-Romero, Manuel

AU - Vallejos, María

AU - Paruelo, José M

AU - Alcaraz-Segura, Domingo

AU - Torres-García, M Trinidad

AU - Salinas-Bonillo, María J.

AU - Cabello, Javier

N1 - Funding Information: We thank R Romero-Calcerrada and J M Requena-Mullor for helpful discussions, and three anonymous reviewers for their constructive suggestions to improve this paper. We also thank the Spanish Ministry of Economy and Business (Project CGL2014-61610-EXP) for the financial support, as well as the Spanish Ministry of Education for the FPU Predoctoral Fellowship of MPR (FPU14/06782) and MTTG (16/02214). MPR gratefully acknowledges funding from Universidad de Almería for a research stay at the Laboratory of Regional Analysis and Remote Sensing (LART) of University of Buenos Aires to develop this study. This research was done within the LTSER Platforms of the Arid Iberian South East—Spain (LTER_EU_ES_027) and Sierra Nevada/Granada (ES- SNE)—Spain (LTER_EU_ES_010), and contributes to the Global Land Programme. Publisher Copyright: © 2022 The Author(s). Published by IOP Publishing Ltd.

PY - 2022/4/1

Y1 - 2022/4/1

N2 - The spatial mapping of social-ecological system (SES) archetypes constitutes a fundamental tool to operationalize the SES concept in empirical research. Approaches to detect, map, and characterize SES archetypes have evolved over the last decade towards more integrative and comparable perspectives guided by SES conceptual frameworks and reference lists of variables. However, hardly any studies have investigated how to empirically identify the most relevant set of indicators to map the diversity of SESs. In this study, we propose a data-driven methodological routine based on multivariate statistical analysis to identify the most relevant indicators for mapping and characterizing SES archetypes in a particular region. Taking Andalusia (Spain) as a case study, we applied this methodological routine to 86 indicators representing multiple variables and dimensions of the SES. Additionally, we assessed how the empirical relevance of these indicators contributes to previous expert and empirical knowledge on key variables for characterizing SESs. We identified 29 key indicators that allowed us to map 15 SES archetypes encompassing natural, mosaic, agricultural, and urban systems, which uncovered contrasting land sharing and land sparing patterns throughout the territory. We found synergies but also disagreements between empirical and expert knowledge on the relevance of variables: agreement on their widespread relevance (32.7% of the variables, e.g. crop and livestock production, net primary productivity, population density); relevance conditioned by the context or the scale (16.3%, e.g. land protection, educational level); lack of agreement (20.4%, e.g. economic level, land tenure); need of further assessments due to the lack of expert or empirical knowledge (30.6%). Overall, our data-driven approach can contribute to more objective selection of relevant indicators for SES mapping, which may help to produce comparable and generalizable empirical knowledge on key variables for characterizing SESs, as well as to derive more representative descriptions and causal factor configurations in SES archetype analysis.

AB - The spatial mapping of social-ecological system (SES) archetypes constitutes a fundamental tool to operationalize the SES concept in empirical research. Approaches to detect, map, and characterize SES archetypes have evolved over the last decade towards more integrative and comparable perspectives guided by SES conceptual frameworks and reference lists of variables. However, hardly any studies have investigated how to empirically identify the most relevant set of indicators to map the diversity of SESs. In this study, we propose a data-driven methodological routine based on multivariate statistical analysis to identify the most relevant indicators for mapping and characterizing SES archetypes in a particular region. Taking Andalusia (Spain) as a case study, we applied this methodological routine to 86 indicators representing multiple variables and dimensions of the SES. Additionally, we assessed how the empirical relevance of these indicators contributes to previous expert and empirical knowledge on key variables for characterizing SESs. We identified 29 key indicators that allowed us to map 15 SES archetypes encompassing natural, mosaic, agricultural, and urban systems, which uncovered contrasting land sharing and land sparing patterns throughout the territory. We found synergies but also disagreements between empirical and expert knowledge on the relevance of variables: agreement on their widespread relevance (32.7% of the variables, e.g. crop and livestock production, net primary productivity, population density); relevance conditioned by the context or the scale (16.3%, e.g. land protection, educational level); lack of agreement (20.4%, e.g. economic level, land tenure); need of further assessments due to the lack of expert or empirical knowledge (30.6%). Overall, our data-driven approach can contribute to more objective selection of relevant indicators for SES mapping, which may help to produce comparable and generalizable empirical knowledge on key variables for characterizing SESs, as well as to derive more representative descriptions and causal factor configurations in SES archetype analysis.

KW - Biology

KW - coupled human and natural systems,

KW - essential social-ecological system variable

KW - , human-environment interactions

KW - long-term social-ecological research

KW - LTSER

KW - random forest

KW - social-ecological system framework

UR - https://www.mendeley.com/catalogue/837c73a2-dfbf-3ea8-95dc-391445f673c5/

UR - http://www.scopus.com/inward/record.url?scp=85128170900&partnerID=8YFLogxK

U2 - 10.1088/1748-9326/ac5ded

DO - 10.1088/1748-9326/ac5ded

M3 - Journal articles

VL - 17

JO - Environmental Research Letters

JF - Environmental Research Letters

SN - 1748-9318

IS - 4

M1 - 045019

ER -